Artificial intelligence/machine learning estimation of FAO and ASCE standardized reference ET erodes the physical basis and intended purpose of ET standardization

Assessing integrated water status in drip-irrigated maize fields using UAV multispectral data and machine learning algorithms

Mingjie Ma, Jinghua Zhao, Ming Hong, Tingrui Yang, Qiuping Fu

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning

Moshe Dubinin, M. Morozov, O. Keisar, G. Lidor, Victor Alchanatis, Avi Sadka, et al.

Deep learning-based response mechanism of nitrogen utilization, yield, and quality in mulched drip irrigation rice to nitrogen application rate

Jinyu He, Xianyuan Bao, Sikai Li, Dengyu Zhang, Hailin Yang, Q. Yang, et al.

An explainable deep learning framework for sub-seasonal to seasonal agricultural drought prediction over India

Saurabh Verma, L. Karthikeyan, Subimal Ghosh

Estimation of cotton plant moisture content using UAV multimodal data and machine learning

Zhenyang Wang, Caixia Yin, Siyuan Chen, Hao Zhang, Maoguang Chen, Tao Lin, et al.

An integrated satellite remote sensing approach for mapping field-scale irrigation dynamics to support water management in highly regulated semi-arid catchments

Jackline W. Muturi, Sayantan Majumdar, Christopher E. Ndehedehe, Nathan O. Agutu, Mark J. Kennard

Accurate monitoring of the spatio-temporal extent of irrigated croplands is critical for effective water management. Because vegetation greenness alone cannot distinguish irrigation from rainfall-driven growth, satellite-based detection of irrigated systems remains challenging. T…Read more